Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System. (arXiv:2201.12604v2 [cs.LG] UPDATED)
Humans excel at continually learning from an ever-changing environment
whereas it remains a challenge for deep neural networks which exhibit
catastrophic forgetting. The complementary learning system (CLS) theory
suggests that the interplay between rapid instance-based learning and slow
structured learning in the brain is crucial for accumulating and retaining
knowledge. Here, we propose CLS-ER, a novel dual memory experience replay (ER)
method which maintains short-term and long-term semantic memories that interact
with the episodic memory. Our method employs an effective replay mechanism
whereby new knowledge is acquired while aligning the decision boundaries with
the semantic memories. CLS-ER does not utilize the task boundaries or make any
assumption about the distribution of the data which makes it versatile and
suited for "general continual learning". Our approach achieves state-of-the-art
performance on standard benchmarks as well as more realistic general continual
learning settings.
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